A Multicast Protocol for Content-Based Publish-Subscribe Systems

The publish/subscribe (or pub/sub) paradigm is a simple and easy to use model for interconnecting applications in a distributed environment. Many existing pub/sub systems are based on pre-defined subjects, and hence are able to exploit multicast technologies to provide scalability and availability. An emerging alternative to subject-based systems, known as content-based systems, allow information consumers to request events based on the content of published messages. This model is considerably more flexible than subject-based pub/sub, however it was previously not known how to efficiently multicast published messages to interested content-based subscribers within a network of broker (or router) machines. In this paper, we develop and evaluate a novel and efficient technique for multicasting within a network of brokers in a content-based subscription system, thereby showing that content-based pub/sub can be deployed in large or geographically distributed settings

Implementation of a Radial Basis Function Using VHDL

This paper presents the work regarding the implementation of neural network using radial basis function algorithm on very high speed integrated circuit hardware description language (VHDL). It is a digital implementation of neural network. Neural Network hardware has undergone rapid development during the last decade. Unlike the conventional von-Neumann architecture that is sequential in nature, Artificial Neural Networks (ANNs) Profit from massively parallel processing. A large variety of hardware has been designed to exploit the inherent parallelism of the neural network models. The radial basis function (RBF) network is a two-layer network whose output units form a linear combination of the basis function computed by the hidden uni